1. Field of Invention
The current invention relates to automated identification of soft tissue substructures in a soft tissue region of a human or animal subject in a non-invasive manner.
2. Discussion of Related Art
It is highly desirable to have automated identification of soft tissue substructures in soft tissue regions of a human and possibly animal subject in a non-invasive manner. For example, diagnosing brain pathologies associated with white matter brain tissue can benefit from such automated identification. Three dimensional white matter fiber tract reconstruction based on diffusion tensor imaging (DTI) is becoming a useful tool in research and clinical studies. (See, for example, U.S. Pat. Nos. 5,539,310 and 6,526,305.) Currently, it is the only method to reconstruct trajectories of white matter fiber tracts non-invasively. However, existing methods of fiber reconstruction require a substantial amount of anatomical knowledge of the brain white matter in order to extract only the tract regions of interest. Because a straightforward reconstruction of all white matter yields a huge amount of fiber tracts entangled inside the brain, it does not have practical value in such an unprocessed form. An operator must have a substantial amount of anatomical knowledge of the brain white matter in order to extract a specific tract from the entangled reconstructed fiber tracts or to prune the regions being reconstructed beforehand. However, experienced operators with detailed knowledge of brain white matter anatomy may not be readily available, which may present a bottleneck in the application of fiber reconstruction technology in research and clinical studies. Further, suboptimal quality of diffusion tensor imaging data may hamper the ability of even the experienced operators in using existing methods. Suboptimal quality may result from, for example, breathing motion of the subject during data acquisition. Therefore, there is thus a need for a system and method for automated tracking of fiber tracts in human brain white matter using diffusion tensor imaging.